Protein Function Prediction for Omics Era

by Kihara, Daisuke

• ISBN13: 9789400708808
• ISBN10: 9400708807
• Format: Hardcover
• Copyright: 2011-04-25
• Publisher: Springer Verlag

Summary

Gene function annotation has been a central question in molecular biology. The importance of computational function prediction is increasing because more and more large scale biological data, including genome sequences, protein structures, protein-protein interaction data, microarray expression data, and mass spectrometry data, are awaiting biological interpretation. Traditionally when a genome is sequenced, function annotation of genes is done by homology search methods, such as BLAST or FASTA. However, since these methods are developed before the genomics era, conventional use of them is not necessarily most suitable for analyzing a large scale data. Therefore we observe emerging development of computational gene function prediction methods, which are targeted to analyze large scale data, and also those which use such omics data as additional source of function prediction. In this book, we overview this emerging exciting field. The authors have been selected from 1) those who develop novel purely computational methods 2) those who develop function prediction methods which use omics data 3) those who maintain and update data base of function annotation of particular model organisms (E. coli), which are frequently referred

Table of Contents

Computational Protein Function Prediction: Framework and Challenges	p. 1
Enhanced Sequence-Based Function Prediction Methods and Application to Functional Similarity Networks	p. 19
Gene Cluster Prediction and Its Application to Genome Annotation	p. 35
Functional Inference in Microbial Genomics Based on Large-Scale Comparative Analysis	p. 55
Predicting Protein Functional Sites with Phylogenetic Motifs: Past, Present and Beyond	p. 93
Exploiting Protein Structures to Predict Protein Functions	p. 107
Sequence Order Independent Comparison of Protein Global Backbone Structures and Local Binding Surfaces for Evolutionary and Functional Inference	p. 125
Protein Binding Ligand Prediction Using Moments-Based Methods	p. 145
Computational Methods for Predicting DNA-Binding Sites at a Genomic Scale	p. 165
Electrostatic Properties for Protein Functional Site Prediction	p. 183
Function Prediction of Genes: From Molecular Function to Cellular Function	p. 197
Predicting Gene Function Using Omics Data: From Data Preparation to Data Integration	p. 215
Protein Function Prediction Using Protein-Protein Interaction Networks	p. 243
KEGG and GenomeNet Resources for Predicting Protein Function from Omics Data Including KEGG PLANT Resource	p. 271
Towards Elucidation of the Escherichia coli K-12 Unknowneome	p. 289
Index	p. 307
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